EnergyVis: Interactively Tracking and Exploring Energy Consumption for ML Models

  • Omar Shaikh ,
  • Jon Saad-Falcon ,
  • Austin P Wright ,
  • Nilaksh Das ,
  • ,
  • Omar Asensio ,
  • Duen Horng (Polo) Chau

2021 Human Factors in Computing Systems |

Published by ACM

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The EnergyVis user interface, with multiple coordinated views. (A) The Model Energy Profile View allows users to select an energy profile of pre-loaded models, generate new profiles (for models that a user wishes to train), and import saved profiles. (B) The Consumption Chart allows users to view the energy and carbon consumption of their selected model. (C) Using the Model Region view, users can view the region where a model was trained, and select regions with a lower energy intensity as an alternative to reduce emissions. (D) Users can expand the Colored Equations for succinct descriptions of various variables and how they contribute to calculating a model’s emissions. (E) Finally, users can view or adjust hardware used to train a model using Alternative Hardware.

The advent of larger machine learning (ML) models have improved state-of-the-art (SOTA) performance in various modeling tasks, ranging from computer vision to natural language. As ML models continue increasing in size, so does their respective energy consumption and computational requirements. However, the methods for tracking, reporting, and comparing energy consumption remain limited. We present EnergyVis, an interactive energy consumption tracker for ML models. Consisting of multiple coordinated views, EnergyVis enables researchers to interactively track, visualize and compare model energy consumption across key energy consumption and carbon footprint metrics (kWh and CO2), helping users explore alternative deployment locations and hardware that may reduce carbon footprints. EnergyVis aims to raise awareness concerning computational sustainability by interactively highlighting excessive energy usage during model training; and by providing alternative training options to reduce energy usage.